Abstract: There is provided a method and system for training an object recognition machine learning model to perform object recognition in data acquired by ultrawide field of view (UW FOV) sensors to thereby obtain a distortion-aware object recognition model. The object recognition model comprises convolution layers each associated with a set of kernels. During training on a UW FOV labelled training dataset, deformable kernels are learned in a manifold space, mapped back to Euclidian space and used to perform convolutions to obtain output feature maps which are used to perform object recognition predictions. Model parameters of the distortion-aware object recognition model may be transferred to other architectures of object recognition models, which may be further compressed for deployment on embedded systems such as electronic devices on board autonomous vehicles.

Abstract: There is provided a method and a system for customized image denoising with interpretability. A deep neural network (NN) is trained to denoise an image on a training dataset including pairs of noisy and corresponding clean images acquired from an imaging apparatus, where during the training a structured covariance score (SCS) indicative of a performance of the deep NN in recovering content of corresponding clean images relative to the denoised image is determined based on sparse conditional correlations. A test noisy image is received and denoised by the deep NN. A user feedback score indicative of user satisfaction of the denoising is obtained. A quality parameter is obtained based on the SCS and a quality metric indicative of denoised image quality is obtained from a pretrained NN, and compared with the user feedback score. If the SCS is above the user feedback score, the deep NN is provided for denoising.

Abstract: The present invention is directed to a method for detection of microbial colonies on a surface, comprising the steps of obtaining one or a plurality of digital images I0, I1, I2, I3, I4 of the surface, said digital images I0, I1, I2, I3, I4 being represented by at least two-dimensional matrices of pixel values, calculating a statistical noise distribution based on at least one of the digital images I0, applying the statistical noise distribution calculated in the calculation step to the one or the plurality of digital images I0, I1, I2, I3, I4, and detecting an object of interest as a candidate for a microbial colony based on deviation of pixel values from the noise distribution.

Abstract: The present invention is directed to a method for detection of microbial colonies on a surface, comprising the steps of obtaining one or a plurality of digital images I0, I1, I2, I3, I4 of the surface, said digital images I0, I1, I2, I3, I4 being represented by at least two-dimensional matrices of pixel values, calculating a statistical noise distribution based on at least one of the digital images I0, applying the statistical noise distribution calculated in the calculation step to the one or the plurality of digital images I0, I1, I2, I3, I4, and detecting an object of interest as a candidate for a microbial colony based on deviation of pixel values from the noise distribution.